Integrated omics analyses reveal natamycin disrupts oxidative phosphorylation pathways in Magnaporthe oryzae

Abstract

Magnaporthe oryzae, the causative agent of rice blast, is a devastating fungal pathogen that threatens global rice production. While natamycin is a known antimicrobial compound of microbial origin, its efficacy and precise mode of action against M. oryzae have remained largely unexplored. In this study, treatment with various concentrations of natamycin potently inhibited mycelial growth by 16.43% to 100%, with an EC₅₀ value of 11.77 mg/L. Pathogenicity of M. oryzae was also markedly reduced, declining to 4.1% of the control level under both in vitro and greenhouse conditions. To elucidate the underlying mechanisms, we adopted a multi-omics approach, integrating transcriptomic and metabolomic data. This analysis revealed a coordinated disruption of key metabolic processes, including amino acid biosynthesis and, most critically, oxidative phosphorylation. Functional validation suggested the involvement of oxidative phosphorylation, as overexpression of two key genes in this pathway MGG_17901 and MGG_04969 conferred significant natamycin tolerance to the fungus. Consistent with this, the overexpression strains also exhibited enhanced pathogenicity, indicating that MGG_17901 and MGG_04969 are involved in the virulence of M. oryzae. Molecular docking further identified binding sites between natamycin and the proteins encoded by these genes, and sequence alignments across 96 M. oryzae strains confirmed that these sites are conserved. Collectively, our findings suggest that natamycin disrupts these pathways and underscore its potential as a sustainable, naturally derived biocontrol agent for the management of rice blast disease.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12870-026-09573-9.

Introduction

Rice (Oryza sativa L.) is one of the world’s most important staple cereal crops. Its production is consistently challenged by various pathogenic microorganisms, including bacterial, fungal, and viral pathogens, which lead to substantial annual yield losses. Among these, Magnaporthe oryzae, one of the most devastating fungal pathogens of rice, causes significant yield reductions and poses a serious threat to global rice production [1]. Current control strategies rely heavily on chemical pesticides such as mancozeb [2], while the development and deployment of resistant rice varieties also represent effective management approaches [3, 4]. However, resistance conferred by plant varieties is often overcome by the pathogen over time [5]. Furthermore, the overuse of pesticides raises environmental and food safety concerns, underscoring the need to identify newer and safer alternatives for plant disease management.

Overreliance on pesticides, despite their role in securing food production, has resulted in widespread environmental contamination. These pollutants adversely affect water quality, biodiversity, and human health [6]. A major concern is their impact on soil ecosystems, where they threaten microbial communities that drive essential functions such as nutrient cycling and soil organic matter formation [7, 8]. Microorganisms themselves represent valuable bio‑resources that can be used directly or indirectly for biocontrol purposes [9]. Biological control of plant diseases offers an efficient and eco‑friendly alternative to synthetic fungicides [10, 11]. For instance, antimicrobial peptides derived from bacteria exhibit broad‑spectrum activity against diverse pathogens, including bacteria, fungi, and viruses [12]. Different microbial‑derived compounds act through distinct mechanisms. Bacitracin, produced by Bacillus subtilis, inhibits peptidoglycan synthesis, thereby compromising cell wall integrity in pathogens such as Staphylococcus [12, 13]. In contrast, gramicidin S, from Aneurinibacillus migulanus, targets the plasma membrane by forming oligomeric β‑barrel pores that disrupt membrane integrity and lead to cell death [14, 15].

Natamycin (C₃₃H₄₇NO₁₃) is a natural antimicrobial peptide produced by strains of Streptomyces natalensis [16]. It has been widely used as a food additive to prevent microbial contamination [17], and exhibits in vitro antimicrobial activity against organisms such as Aspergillus niger, Escherichia coli, and Clostridium botulinum [18]. Its antifungal activity against molds is well documented, and it is commercially employed as a preservative in fermented foods like sausages and cheese to inhibit mold growth [19].

Although natamycin is a well‑established antimicrobial agent, its precise mode of antifungal action against phytopathogens particularly the rice blast fungus M. oryzae remains poorly characterized. We hypothesized that an integrated multi‑omics approach, combining transcriptomics and metabolomics, could elucidate the mechanistic basis of natamycin‑mediated inhibition of M. oryzae. Our study suggests remarkable antifungal activity of natamycin against M. oryzae under both in vitro and greenhouse conditions. Furthermore, we reveal a novel, multi‑target mechanism of action involving disruption of oxidative phosphorylation and amino acid biosynthesis pathways. These findings provide critical insights into the potential of natamycin as a sustainable bio‑control agent for managing one of the most devastating diseases in global rice production.

Materials and methods

Greenhouse experiment and plant material

All greenhouse experiments were conducted at the State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, located at Yunnan Agricultural University in Kunming (25° 04′ N, 102° 45′ E), China. The greenhouse conditions were maintained at a day/night temperature of 28°C/22°C. The rice variety Lijiangxintuanheigu (LTH) obtained from Yunnan Agricultural University, Kunming, China was used as the plant material for all greenhouse experiments.

In-vitro antifungal assessment of natamycin against M. oryzae

The M. oryzae strain H7, originally isolated in 2022 from a rice blast lesion collected in Yunnan Province, China, was maintained in the Molecular Plant Pathology Laboratory at Yunnan Agricultural University and used for all experiments. To evaluate the antifungal activity of natamycin (99.07% purity; MedChemExpress, Shanghai, China), the compound was tested against M. oryzae H7 at varying concentrations (5, 10, 20, and 40 mg/L). Complete medium (CM) agar plates supplemented with each concentration were inoculated with the fungal mycelium. The plates were sealed with Parafilm tape and incubated at 28 °C for 7 days in an inverted position (n = 3 per treatment). Colony growth was monitored daily, and the diameter of the growth inhibition zone (GIZ) was measured at 7 days post-inoculation.

Sporulation, conidial germination, and appressorium formation assays

To evaluate the effect of natamycin on fungal development, sporulation, conidial germination, and appressorium formation were assessed at 0, 1, 5, 10, 20, and 40 µg/mL.

Sporulation

M. oryzae H7 was cultured in PDB (120 rpm, 28 °C, dark, 3 d). A 600 µL aliquot was spread onto prune juice agar, incubated at 28 °C (dark, 3 d), then transferred to 12 h light/12 h dark at 28 °C for 7 days. Conidia were harvested with 5 mL sterile water and counted using a hemocytometer. These experiments were repeated 3 times.

Germination and appressorium formation

Conidia (1 × 10⁵/mL) were placed on hydrophobic coverslips with natamycin and incubated at 28 °C in a moist chamber. Germination was scored at 4, 8, and 24 h, and appressorium formation. Germination rate (%) = (G/T) × 100; appressorium formation rate (%) = (A/T) × 100, where G = germinated conidia, A = conidia with appressoria, and T = total conidia counted. These experiments were repeated 3 times.

Disease index assays

We then evaluated the effects of natamycin treatment on LTH rice plants inoculated with M. oryzae. To prepare the inoculum, filter paper containing M. oryzae H7 strain was first cultivated on CM for a period of 8 to 12 days. Following incubation, spores were collected by gently scraping the culture with a solution of sterile distilled water with 0.01% Tween 20. The spore suspension was adjusted to a standard concentration of 2 × 10⁶ spores/mL. For inoculation, 10 mL of this prepared suspension was applied to 21-day-old seedlings of the susceptible rice cultivar LTH [20]. Immediately after pathogen inoculation, 40 mg/L of natamycin was applied by spraying onto the seedlings, while sterilized distilled water was used as a control (CK). Disease incidence was assessed 7 days post-inoculation using a 0–5 severity scale.

RNA extraction, sequencing, GO and KEGG enrichment analysis

The mycelium of M. oryzae was cultivated into liquid CM for 2 days and transferred into a new liquid CM, adding 40 mg/L natamycin for 24 h with three biological replications, mycelia without the treatment were used as the control. Total RNA extraction and sequencing were performed according to the methods described by published paper [21]. The clean reads were mapped to the M. oryzae 70 − 15 reference genome. Differential expression analysis was performed using DESeq2, and genes with |log₂FC| ≥ 1 and padj < 0.05 were considered differentially expressed genes (DEGs). The list of DEGs was submitted to ClusterProfiler (v4.14.4) for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, with significantly enriched pathways identified at p < 0.05 [1].

Network correlation analyses of DEGs

The network analyses of up and downregulated DEGs were conducted based on the Pearson correlation coefficient and visualized using Gephi software [22]. Network topological parameters, including average degree and modularity, were also calculated using Gephi. To functionally characterize the identified network modules, KEGG pathway enrichment analysis was performed using the DAVID online tools.

Metabolite extraction

Metabolite profiling was performed by Majorbio Bio-Pharm Technology Co. Ltd., located in Shanghai, China. Metabolite extraction followed RNA sequencing sample preparation principles. For each treatment, 100 µL of liquid sample was combined with 800 µL of extraction solvent (acetonitrile: methanol, 1:1 v/v) containing internal standards in a 1.5 mL centrifuge tube. Samples were vortex-mixed (30 s) and sonicated at low temperature (5 °C, 40 kHz, 30 min), then incubated at -20 °C (30 min) for protein precipitation. After centrifugation (13,000 g, 4 °C, 15 min), the supernatant was collected, evaporated under nitrogen, and reconstituted in 100 µL of acetonitrile: water (1:1). The solution underwent low-temperature sonication (5 °C, 40 kHz, 5 min) and centrifugation (13,000 g, 4 °C, 10 min). The final supernatant was transferred to vials for ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) analysis.

Metabolomics analysis

Metabolite profiling was performed using UHPLC-MS/MS on a Thermo UHPLC-Q Exactive HF-X system equipped with an ACQUITY HSS T3 column (100 mm × 2.1 mm, 1.8 μm; Waters, USA) and an electrospray ionization (ESI) source operating in dual positive/negative modes [23]. Raw data were processed in Progenesis QI (Waters), with metabolites identified by spectral matching against (http://www.hmdb.ca/), Metlin (https://metlin.scripps.edu/), and the Majorbio database (https://www.majorbio.com/flow). Differential metabolites were pinpointed via PCA and PLS-DA modeling. Differential metabolites were identified by combining OPLS-DA-derived VIP scores (VIP > 1) and Student’s t-test results (p < 0.05). These metabolites were mapped to KEGG pathways for functional classification and enrichment analysis, which evaluated pathway over-representation by shifting from single-metabolite to group-level annotation [24].

Integrated transcriptomic and metabolomics analysis

The KEGG pathway of transcriptome and metabolites was compared using Venn diagram. The pathway rich in transcriptome and metabolites was selected for analysis. KEGG analyses were conducted in the DAVID online tools to study the regulation of genes with their corresponding metabolites. The link between genes and metabolites of the most abundant pathways in each treatment was visualized.

qRT-PCR

To validate the transcriptomic data, quantitative real-time PCR (qRT-PCR) was performed on selected differentially expressed genes. To synthesize first-strand cDNA, 2 µg of RNA was used as the template, and the TransScript^® One-Step gDNA Removal and cDNA Synthesis SuperMix TransScript kit (TransGen, Beijing, China) was employed according to the manufacturer’s instructions. For quantitative real-time PCR (qRT-PCR), a 20 µl reaction mixture was prepared, containing 10 µl of PerfectStart^® Green qPCR SuperMix (TransGen, Beijing, China), 0.4 µl of forward and reverse primers (each at a concentration of 10 µM), and 3 µl of the synthesized cDNA template. The qRT-PCR was performed on the CFX96 Real-Time PCR system (Bio-Rad). The relative fold changes in gene expression levels of fungi treated with compounds were calculated using the 2^−ΔΔCt method, where ΔΔCt = [(Ct, target gene − Ct, actin) treatment − (Ct, target gene − Ct, actin) control]. Each qRT-PCR experiment was conducted with three replicates. The primer pairs used for qRT-PCR were independently designed to assess the relative expression levels of the selected genes, and the detailed information of these primers is listed in Table S1.

Overexpressed strain construction

The overexpression strains for candidate genes MGG_17901 and MGG_04969 were constructed following established protocols described by [25]. Briefly, the open reading frame (ORF) sequences of each gene were cloned into the pYF11 vector under the control of the strong constitutive RP27 promoter. Each resulting overexpression vector was verified by sequencing and subsequently transformed into protoplasts of the M. oryzae H7 strain using the PEG–CaCl₂-mediated method. Transformants were selected on appropriate media, and successful overexpression was confirmed via PCR.

Mitochondrial complex IV activity assay

Mitochondrial complex IV (cytochrome c oxidase) activity was determined using a commercial kit (Suzhou Mengxi Biopharmaceutical Technology Co., Ltd.) according to the manufacturer’s protocol. Following natamycin treatment (40 µg/mL, 24 h), mitochondrial fractions were isolated from M. oryzae mycelia. Enzyme activity was measured spectrophotometrically by monitoring the oxidation of reduced cytochrome c at 550 nm over 5 min at 25 °C. Activity was expressed as nmol cytochrome c oxidized per minute per mg protein.

Molecular docking

Based on the AlphaFold2 predictions, we generated the protein structures of MGG_04969 and MGG_17901. The structure of natamycin was retrieved from PubChem. Molecular docking between the candidate proteins and natamycin was performed using AutoDock Vina. The resulting docking poses were visualized using PyMOL.

Statistical analysis

Statistical analyses were carried out using GraphPad Prism 8.0.2. Multiple-group comparisons were performed using analysis of variance (ANOVA) followed by Dunnett's multiple comparison test, whereas comparisons between two groups were conducted using Student's t-test. Differences were considered statistically significant at p < 0.05. Adobe Illustrator was used to adjust and merge all the figures.

Results

The antifungal activities of natamycin against M.oryzae

Natamycin exhibited potent, dose-dependent inhibition of M. oryzae strain H7. Mycelial growth was significantly suppressed at higher concentrations (Fig. 1A). In vitro colony formation was inhibited by 16.43%, 40.81%, 72.89% and 100% at concentrations of 5, 10, 20, and 40 mg/L, respectively (Fig. 1A-B). The dose-response relationship was fitted with a linear regression (y = 40.729x − 50.404), from which the half-maximal effective concentration (EC₅₀) was determined to be 11.77 mg/L. Besides mycelial growth observation, natamycin also significantly inhibits conidia formation and germination in a dose-dependent manner (Fig. 1C-D). Furthermore, appressorium formation a critical step for host penetration, was severely suppressed compared to the control (Fig. 1E).

Fig. 1.

Antifungal efficacy of natamycin against M. oryzae. A Mycelial growth inhibition on CM under natamycin treatments. B Colony diameter under natamycin treatments. C Number of conidia under natamycin treatments. D Conidial germination rates over 24 h under natamycin treatments E Appressorium formation inhibition under natamycin treatments. F Rice leaves showing disease symptoms under natamycin treatments. G Disease index under natamycin treatments. Statistical significance was determined by ANOVA followed by Dunnett's multiple comparison test (B-E), and by Student's t-test for two-group comparisons (G). Asterisks indicate significant differences among treatments (*p < 0.05; ***p < 0.001; ****p < 0.0001)

This in vitro antifungal efficacy correlated with marked disease control in vivo. Under greenhouse condition, foliar application of natamycin significantly suppressed lesion development on rice leaves compared to the control (Fig. 1F). Consequently, the disease index was drastically reduced from 64.43% in control plants to 4.1% in natamycin-treated plants (Fig. 1G). These results establish natamycin as a highly effective antifungal agent against rice blast, prompting further mechanistic investigation.

RNA sequencing under natamycin in M. oryzae

Having established natamycin’s potent antifungal activity, we employed RNA sequencing to profile the global transcriptional changes in M. oryzae following natamycin exposure. Total RNA was extracted from fungal cultures treated with natamycin and from untreated controls, with three biological replicates per condition. The Illumina HiSeq platform generated a total of 285,854,270 raw reads across all samples, averaging 47,642,378 reads per replicate. After quality filtering, we obtained an average of 46,877,340 high-quality reads per sample, with Q30 scores exceeding 93.87% (Table S2).

Differential expression analysis under natamycin treatments

To define the transcriptomic signature of natamycin in M. oryzae, we identified differentially expressed genes (DEGs) using thresholds of |log₂FC| ≥ 1 and FDR < 0.05. This analysis revealed 6,198 DEGs in natamycin-treated samples versus controls, comprising 3,031 upregulated and 3,167 downregulated genes (Fig. 2A; Table S3). Hierarchical clustering analysis further confirmed that natamycin treatment elicited a strong and distinct transcriptional response, with two clear clusters of DEGs exhibiting significant differential expression compared to the control (Fig. 2B).

Fig. 2.

Overview of differentially expressed genes (DEGs) in response to natamycin treatment. Volcano plot showing DEGs identified under natamycin treatment conditions (A). Hierarchical clustering analysis of DEGs across treatments, illustrating global gene expression patterns and treatment-specific transcriptional responses (B). “Up” indicates upregulated genes, “Down” indicates downregulated genes, and “No” denotes genes with no significant change in expression

Functional annotation and pathway enrichment of differentially expressed genes

To functionally characterize the DEGs, we performed GO and KEGG enrichment analyses. According to GO analysis, DEGs were classified into biological processes (such as cellular processes, metabolic processes, biological regulation, and single organism processes), cellular components (including cells, cell parts, macromolecular components, and organelles), and molecular functions (such as binding, catalytic activity, and transporter activity) (Figure S1).

Analysis of up- and downregulated GO terms revealed distinct functional patterns between the two gene sets. GO term analysis showed that upregulated genes were significantly enriched in functions related to organelle membranes (vacuole, Golgi apparatus, peroxisome) and biological processes such as autophagy, peroxisome organization, and lipid modification (Fig. 3A). In contrast, downregulated genes were predominantly associated with essential biosynthetic and energy-producing functions, including cellular amide and peptide metabolic processes, ribosome assembly and translation, mitochondrial components, and catalytic activities such as ligase and lyase functions (Fig. 3B). These results indicate that natamycin treatment severely disrupts fungal protein synthesis and ATP production.

Fig. 3.

GO and KEGG enrichment analysis of differentially expressed genes in Magnaporthe oryzae under natamycin treatment. GO and KEGG pathway enrichment profiles of upregulated (A) and downregulated (B) DEGs in response to natamycin treatment

KEGG pathway enrichment analysis identified 18 significantly enriched pathways under natamycin treatment (Table S4). Upregulated genes were prominently associated with peroxisome, autophagy, fatty acid degradation, inositol phosphate metabolism, phosphatidylinositol signaling system, and ABC transporters (Fig. 3A), suggesting a compensatory stress response involving lipid metabolism and cellular recycling.

Conversely, downregulated genes were enriched in pathways essential for growth and survival, including amino acid biosynthesis, secondary metabolite biosynthesis, aminoacyl-tRNA biosynthesis, ribosome, cofactor biosynthesis, purine metabolism, oxidative phosphorylation and the TCA cycle (Fig. 3B). These results suggest that natamycin’s primary antifungal effect involves severe disruption of protein synthesis and energy production. The KEGG results align with the GO analysis, consistently highlighting severe disruption of amino acid biosynthesis and mitochondrial functions particularly oxidative phosphorylation within the mitochondrial inner membrane.

Natamycin disrupts pathogenicity related gene expressions

Given natamycin’s strong inhibition of growth and pathogenicity (Fig. 1), we analyzed the expression of pathogenicity-related genes and identified 25 known genes, among which 12 upregulated and 13 downregulated (Table S5). Among the downregulated pathogenicity genes were several key virulence determinants including MoChi1 (MGG_08054) an extracellular chitinase of M. oryzae, which is involved in regulating the growth and pathogenicity of aerial hyphae, and further affects pathogenicity by inhibiting the expression of rice defense genes and the production of chitin-triggered ROS, MoRgs3 (MGG_03726) an RGS protein that is involved in the regulation of pathogenicity and negatively regulates the production of conidia. MoSsc1 (MGG_04191) a component of heat shock protein (Hsp70s), which weakens cell wall integrity signaling and participates in the regulation of vegetative growth and pathogenicity and MoCAPA (MGG_12818) involved in the regulation of polar growth, conidia formation, complete virulence, and endocytosis. 12 upregulated genes including MoMck1 (MGG_00883) a conserved MAP kinase that acts on autophagy and is involved in the regulation of cell wall integrity and pathogenicity, MoGsk1 (MGG_12122) a protein kinase involved in the regulation of autophagy and pathogenicity and is important for phosphorylation of MoHat1 and MoAtg8 (MGG_01062) an autophagy-related protein involved in the regulation of autophagy and is the most critical and useful marker for autophagy induction and the formation of associated vesicle compartments. These results suggest that natamycin treatment could influence the pathogenicity in M. oryzae.

Gene co-expression network analysis

To further elucidate the transcriptional response to natamycin, we constructed gene co-expression networks for upregulated and downregulated DEGs. Notably, the downregulated network exhibited significantly higher complexity than the upregulated network, containing 1,050 nodes and 13,302 edges compared to 678 nodes and 2,163 edges in the upregulated network (Fig. 4A-B; Figure S2).

Fig. 4.

Protein-protein interaction network and functional enrichment analysis of differentially expressed genes in M. oryzae under natamycin treatment. Networks derived from upregulated (A) and downregulated (B) DEGs. KEGG enrichment of modules in upregulated (C) and downregulated (D) networks

To functionally characterize the gene co-expression, a total of 7 modules from the networks were selected due to the nature of their higher complexity, which include black, purple, pink, green, orange, brown and blizzard blue and annotated using KEGG database. The results revealed a distinct contrast in functional enrichment between the networks. The downregulated network was functionally richer, with modules enriched for 58 pathways compared to 33 in the upregulated network. Functional analysis of the network modules was used to identify the five most significant KEGG pathways with higher number of gene count in both the upregulated and downregulated gene sets. In the upregulated modules, pathways with the highest number of gene counts were peroxisome (purple), autophagy-yeast (brown), spliceosome (pink), autophagy-other (brown), and basal transcription factors (black) (Fig. 4C). In the downregulated network, pathways with the highest gene counts corresponded to the biosynthesis of secondary metabolites (pink), ribosome (purple), biosynthesis of amino acids (pink), oxidative phosphorylation (orange), and metabolic pathways (orange) (Fig. 4D).

We identified the top 20 hub genes with the highest connectivity in each network. Downregulated hub genes were predominantly assigned to ribosome (12 genes), oxidative phosphorylation (3 genes), and amino acid biosynthesis (5 genes) (Table S6). This network analysis strongly suggests that oxidative phosphorylation and amino acid biosynthesis are critical pathways targeted by natamycin.

Natamycin triggers metabolomic reprogramming in M. oryzae

Our transcriptomic analysis revealed that natamycin treatment significantly downregulated genes involved in core biosynthetic and energy‑producing pathways, especially amino acid metabolism and oxidative phosphorylation. To test transcriptional signature corresponds to biochemical disruption, we performed metabolomic profiling using UHPLC‑MS/MS.

The metabolomic profile was profoundly altered by natamycin. Partial least squares‑discriminant analysis (PLS‑DA) showed clear separation between natamycin‑treated and control samples (Fig. 5A), with permutation tests confirming model robustness (Fig. 5B). Across all samples, 2,748 metabolites were identified, of which 1,133 were classified as differentially accumulated metabolites (DAMs): 721 were upregulated and 412 were downregulated in the natamycin‑treated group compared to the control (Fig. 5C). Correlation analysis further illustrated distinct clustering of DAMs between the two conditions (Fig. 5D). Together, these results suggest that natamycin induces extensive reprogramming of the M. oryzae metabolome, leading to significant accumulation or reduction of specific metabolites.

Fig. 5.

Metabolite accumulation profiles in M. oryzae under natamycin treatment. Classification of DAMs in M. oryzae under natamycin treatment, along with PLS-DA analysis (A). OPLS-DA S-plot illustrating metabolite discrimination under natamycin treatment (B). Volcano plot showing significantly upregulated and downregulated DAMs in response to natamycin (C). Pearson correlation heatmap of all detected metabolites under natamycin treatment (D). Classification of identified metabolites (E) and KEGG pathway enrichment analysis of DAMs (F)

Most notably, natamycin triggered a downregulation of metabolites (Fig. 5D). The DAMs encompassed diverse functional categories, including carboxylic acids, amino acids, nucleotides, monosaccharides, neurotransmitter amines, cofactors, phospholipids, and eicosanoids (Fig. 5E). KEGG-based annotation confirmed these classifications and further mapped the DAMs to 79 metabolic pathways 40 upregulated and 39 downregulated (Table S7). Notably, upregulated pathways included glyoxylate and dicarboxylate metabolism, arginine biosynthesis, the TCA cycle, and alanine/aspartate/glutamate metabolism, suggesting a compensatory activation of alternative carbon and nitrogen sources. In stark contrast, natamycin markedly suppressed pathways central to energy metabolism and biosynthesis, including purine metabolism, nucleotide metabolism, cofactor biosynthesis, glutathione metabolism, ABC transporters, arginine and proline metabolism, and oxidative phosphorylation (Fig. 5F). This pronounced downregulation of energetic and anabolic pathways aligns with our transcriptomic findings and underscores the metabolic collapse induced by natamycin.

Natamycin strongly targets the oxidative phosphorylation and amino acid biosynthesis pathways at the transcriptome and metabolomics levels.

Integrated transcriptome and metabolomic analysis showed that 65 KEGG shared pathways (Figure S3A), including biosynthesis of cofactors, purine metabolism, arginine and proline metabolism, oxidative phosphorylation, histidine metabolism, valine, leucine and isoleucine biosynthesis, aminoacyl tRNA biosynthesis and autophagy pathways (Figure S3B). Focusing on consistently downregulated pathways, we identified oxidative phosphorylation, purine metabolism, arginine and proline metabolism, biosynthesis of cofactors, phenylalanine metabolism, glutathione metabolism, valine leucine and isoleucine biosynthesis, autophagy, and endocytosis as jointly downregulated pathways (Fig. 6A). Among these, three pathways, oxidative phosphorylation, arginine and proline metabolism, and valine, leucine, and isoleucine biosynthesis, were selected for detailed mapping.

Fig. 6.

Integrated multi-omics analysis of M. oryzae under natamycin treatment. Downregulated KEGG pathways derived from combined transcriptome–metabolome analysis (A). Integrated visualization of key metabolic pathways, including oxidative phosphorylation (B), valine, leucine and isoleucine biosynthesis (C), and arginine and proline metabolism (D). The text shown in light and dark purple background indicate gene and metabolite, respectively. Red and blue text represent up- and down-regulated metabolites and genes, respectively. The text marked in italics was gene

In the oxidative phosphorylation pathway, natamycin significantly suppressed the accumulation of ADP, a critical metabolite for ATP production. This was accompanied by the coordinated downregulation of multiple genes encoding key complexes, including those for mitochondrial NADH dehydrogenase (MGG_04140), hypothetical protein (MGG_00666), hypothetical protein (MGG_10604), cytochrome c oxidase subunit Via (MGG_17901), ATP synthase subunit alpha (MGG_07752), plasma membrane H⁺-ATPase (MGG_04994), and inorganic pyrophosphatase (MGG_15739) (Fig. 6B).

Similarly, the biosynthesis of valine, leucine, and isoleucine was significantly downregulated through coordinated metabolites and genes expression (Fig. 6C). This metabolic suppression was driven by the concerted downregulation of genes encoding biosynthetic enzymes. For valine and isoleucine biosynthesis, this included downregulation of threonine dehydratase (MGG_07224), acetolactate synthase (MGG_01104), ketol-acid reductoisomerase (MGG_15774), dihydroxy-acid dehydratase (MGG_01139, MGG_05345), and branched-chain-amino-acid aminotransferase (MGG_00942). Leucine biosynthesis exhibited a mixed regulatory pattern, initiated by the downregulation of 2-isopropylmalate synthase (MGG_13485), followed by upregulation of 3-isopropylmalate dehydratase (MGG_01553), and subsequent downregulation of 3-isopropylmalate dehydrogenase (MGG_05223) and branched-chain-amino-acid aminotransferase (MGG_02125) (Fig. 6C).

In the arginine and proline metabolism pathway, natamycin treatment significantly suppressed six critical metabolites: spermidine, 4-guanidinobutanamide, L-Glutamic γ-semialdehyde, 4-aminobutanol, proline, and γ-L-Glutamyl-γ-aminobutyrate Fig. 6D). Concurrently, 11 key genes encoding pathway enzymes, including glutamyl-tRNA(Gln) amidotransferase subunit A (MGG_05827), ornithine decarboxylase (MGG_02441), spermidine synthase (MGG_06197), and arginase (MGG_00671) were all significantly downregulated. This strong correlation between metabolite depletion and gene downregulation across multiple pathways suggests that natamycin disrupts core metabolic functions through a coordinated transcriptomic and metabolomic response.

Overexpression of oxidative phosphorylation-related genes enhances the tolerance to natamycin

To validate the transcriptomic data and elucidate the inhibitory mechanisms of natamycin against M. oryzae, we selected 12 genes involved in amino acid biosynthesis, oxidative phosphorylation, and ribosome function based on KEGG analysis for qRT-PCR analysis. Additionally, for natamycin treatment, we examined the expression of three genes related to the oxidative phosphorylation pathway, such as ATP synthase subunit alpha, cytochrome c oxidase subunit Via, and electron transfer protein 1 (Fig. 7A). These findings not only validate the accuracy of the transcriptome data but also highlight the primary pathways targeted by natamycin.

Fig. 7.

Overexpressed strains enhance tolerance to natamycin and pathogenicity. The relative expressions of genes in M. oryzae under natamycin treatment (A). Mitochondrial Complex IV (cytochrome c oxidase) activity in M. oryzae under natamycin treatment (B). Overexpression of MGG_17901 and MGG_04969 oxidative phosphorylation genes restored tolerance to natamycin in M. oryzae (C&D). Pathogenicity of overexpressed strains toward rice seedlings (E&F). Network correlation analysis of the genes MGG_17901 and MGG_04969 (G). Red nodes represent upregulated genes; green nodes represent downregulated genes. Statistical significance was determined by Student's t-test (B) and by ANOVA followed by Dunnett's multiple comparison test (D&F). Asterisks indicate significant differences among treatments (*p < 0.05; **p < 0.001; ****p < 0.0001)

Given the downregulation of oxidative phosphorylation-related genes, particularly MGG_17901 and MGG_04969, we next assessed whether natamycin affects mitochondrial complex IV activity. 40 µg/mL natamycin treatment significantly reduced mitochondrial complex IV activity compared with the control (CK), with the rate of cytochrome c oxidation decreasing from 3.5 to 0.9 nmol·min⁻¹·mg⁻¹ (Fig. 7B). This suggest that natamycin suppresses mitochondrial respiration by inhibiting Complex IV, and likely impairs ATP production, contributing to the observed antifungal effects.

Based on qRT-PCR validation results, we overexpressed two oxidative phosphorylation related genes, MGG_17901 and MGG_04969 in the wild type strain H7. The results showed that overexpressed strains were more tolerant to natamycin (Fig. 7C-D). The overexpression strains were also inoculated onto rice seedlings, resulting in significantly larger lesion areas compared to those inoculated with the wild-type strain (Fig. 7E-F). Collectively, these findings suggest MGG_17901 and MGG_04969, the putative targets of natamycin, as key contributors to the pathogenicity of M. oryzae.

Furthermore, correlation network analysis indicated that these two genes primarily interact with other genes annotated to the oxidative phosphorylation pathway (Fig. 7G; Table S8). Thus, natamycin plays more important role in disrupting the oxidative phosphorylation pathway to inhibit M. oryzae growth.

Molecular docking

Molecular docking results revealed that predicted binding affinities (ΔG) for the best-scoring docking poses were − 9.6 kcal/mol for MGG_17901 and − 8.8 kcal/mol for MGG_04969. Natamycin formed hydrogen bonds with residues ASN-231, SER-289, and SER-476 of MGG_04969 (Fig. 8A). In contrast, a greater number of hydrogen bonds were observed between natamycin and MGG_17901, involving residues ARG-90, ASN-97, ILE-98, VAL-118, and TYR-120 (Fig. 8B). These results suggest that natamycin may bind to these sites and thereby affect protein function.

Fig. 8.

Molecular docking analysis of natamycin with target proteins and assessment of natural amino acid variation. A Docking pose of natamycin with MGG_04969, showing hydrogen bonds to ASN-231, SER-289, and SER-476. B Docking pose with MGG_17901, showing hydrogen bonds to ARG-90, ASN-97, ILE-98, VAL-118, and TYR-120. Polygenetic analyses of MGG_04969 (C) and MGG_17901 (D) among different host lineages of M. oryzae

Furthermore, we analyzed amino acid variations in MGG_04969 and MGG_17901 across 96 M. oryzae strains isolated from different hosts (Table S9-10). In MGG_04969, six mutant amino acid sites were identified: L3F, K64R, R297C, F354S, G379R, and V414I, occurring in 5, 5, 21, 1, 1, and 2 strains, respectively. Notably, none of these mutation sites corresponded to the residues involved in hydrogen bonding with natamycin, indicating that these variations are unlikely to affect the binding between MGG_04969 and natamycin (Fig. 8C). In the case of MGG_17901, the amino acid sequence was completely conserved across all 96 strains (Fig. 8D). Taken together, the molecular docking results demonstrate that natamycin can bind stably to both proteins.

Discussion

Rice blast, caused by M. oryzae, is one of the most devastating diseases of rice worldwide [26]. While fungicides remain a primary control strategy, their ecological impacts have driven the search for sustainable alternatives. Here, we investigated the antifungal efficacy of natamycin against M. oryzae and elucidated its mechanism of action using integrated transcriptomic and metabolomic analyses.

Natamycin effectively inhibits mycelial growth and pathogenicity of M. oryzae

Natamycin, a macrocyclic polyene produced by Streptomyces natalensis, is well-established as a food preservative with broad-spectrum antifungal activity [16, 17]. Previous studies have demonstrated its efficacy against post-harvest pathogens including Penicillium spp., Aspergillus fumigatus, and Colletotrichum fructicola [27–29]. However, its activity against major foliar pathogens such as M. oryzae remained unexplored. Our study fills this gap by demonstrating that natamycin potently inhibits M. oryzae mycelial growth (EC₅₀ = 11.77 mg/L), conidia sporulation, appressorium formation and dramatically reduces disease severity in planta. The finding is consistent with previous report indicating that natamycin as an excellent antifungal activity against F. graminearum [30]. Similarly, natamycin inhibit germ tube formation and growth of Aspergillus niger [31]. This efficacy against a hemibiotrophic foliar pathogen extends natamycin’s known application scope beyond post-harvest preservation to field-based disease management.

Natamycin treatment induces the transcriptome reprogramming in M. oryzae

Transcriptomic analysis revealed that natamycin triggers a widespread shutdown of critical cellular functions, particularly through downregulation of genes involved in ribosome assembly, translation, and mitochondrial energy production (Fig. 3B). The pronounced downregulation of translational machinery aligns with established roles of ribosomes in fungal pathogenicity [32–35], while concurrent suppression of amino acid metabolism mirrors findings that these pathways are critical for hyphal development and infection [36, 37]. Similarly, mitochondrial dysfunction, a well-documented virulence determinant in fungi [38, 39], was evident, consistent with natamycin’s known effect on mitochondrial membrane potential in Leishmania [40], and the dependence of C. glabrata virulence on mitochondrial integrity [41]. This multi-target inhibition distinguishes natamycin from single-pathway antifungals and likely underlies its efficacy by simultaneously compromising virulence factor production and invasive growth.

KEGG pathway analysis further elucidated natamycin’s mechanism, with significant downregulation of ribosome, oxidative phosphorylation, and amino acid biosynthesis pathways, a pattern reminiscent of pyrylium salt xy12, which targets oxidative phosphorylation through mitochondrial complex I in C. albicans [42]. The convergence on amino acid biosynthesis is particularly noteworthy, as this pathway is a validated vulnerability for attenuating fungal virulence [37], its inhibition represents a potent strategy to compromise pathogenicity [43]. A previous study demonstrated that natamycin inhibits the growth of yeast and fungi via inhibition of amino acid and glucose transport via the plasma membrane [44]. In contrast, upregulated pathways including autophagy, peroxisome function, and lipid metabolism suggest a cellular stress response aimed at recycling damaged components, though this compensatory attempt appears insufficient to counter the profound metabolic collapse induced by natamycin [35]. Thus, the overall transcriptomic landscape depicts a state of profound metabolic failure, where the direct, coordinated inhibition of growth-critical pathways overwhelms the pathogen’s capacity for homeostatic resilience.

Protein-protein interactions (PPI) the downregulated network was markedly more extensive and complex than its upregulated counterpart, suggesting a broad disintegration of core cellular architecture. KEGG enrichment of downregulated modules converged on ribosome function, oxidative phosphorylation, and amino acid biosynthesis pathways already implicated as virulence determinants in M. oryzae [21, 45, 46]. Notably, hub genes within this network included enzymes central to arginine metabolism (MoARG1, MoARG56, MoARG7), whose disruption is known to attenuate pathogenicity [47], reinforcing the functional relevance of our findings. In contrast, upregulated hub genes were predominantly associated with autophagy and peroxisomal processes, consistent with a stress-induced salvage response.

Natamycin treatment enhanced metabolite accumulation and regulates key pathways

Untargeted metabolomics revealed that natamycin profoundly remodeled the M. oryzae metabolome, with enhanced accumulation of amino acids, carboxylic acids, phospholipids, and eicosanoids. Among these, certain carboxylic acid derivatives structurally related to 2-heptyl-5-hexylfuran-3-carboxylic acid have previously demonstrated antifungal activity [48], suggesting that natamycin may indirectly promote endogenous inhibitory metabolite production. Concurrently, KEGG enrichment identified significant downregulation of metabolites linked to purine metabolism, amino acid biosynthesis (including arginine, proline, and histidine pathways), glutathione metabolism, and oxidative phosphorylation. This coordinated suppression points to a metabolic starvation strategy, wherein natamycin deprives the pathogen of amino acid building blocks essential for protein synthesis and energy production both critical for growth and virulence [49, 50]. Our findings align with previous reports that disruption of amino acid metabolism compromises fungal pathogenicity [43], and echo the mode of action of antifungal peptide P852 in Fusarium oxysporum [51]. Collectively, these metabolomic data reinforce a multi-mechanistic model in which natamycin suppresses rice blast disease by simultaneously crippling multiple metabolic hubs impairing ribosome-dependent protein synthesis, energy metabolism, and virulence-associated amino acid pathways. Additionally, exploring whether natamycin’s metabolic disruption translates to synergistic effects with existing fungicides or induces metabolic resistance mechanisms will be essential for optimizing its application in field-based disease management.

Natamycin exerts antifungal activity through oxidative phosphorylation disruption

Integrated transcriptomic and metabolomic analyses revealed that natamycin disrupts energy metabolism in M. oryzae through coordinated suppression of oxidative phosphorylation (OXPHOS)the primary ATP-generating pathway essential for fungal growth and virulence [52, 53]. Natamycin downregulated seven key OXPHOS-related genes, including MGG_04140 (NADH dehydrogenase), MGG_17901 (cytochrome c oxidase subunit VIa), MGG_07752 (ATP synthase subunit alpha), and MGG_04994 (plasma membrane H⁺-ATPase), while simultaneously reducing ADP levels, consistent with impaired ATP production. This energy deficit likely underpins the observed inhibition of mycelial growth and pathogenicity. We found that natamycin treatment downregulated seven key enzyme-encoding DEGs, simultaneously reduced ADP levels. The downregulated genes encode the following: MGG_04140 (mitochondrial NADH dehydrogenase), MGG_00666 (hypothetical protein), MGG_10604 (hypothetical protein), MGG_17901 (cytochrome c oxidase subunit VIa), MGG_07752 (ATP synthase subunit alpha), MGG_04994 (plasma membrane H⁺-ATPase), and MGG_15739 (inorganic pyrophosphatase) (Fig. 6B). Studies have shown that inhibition of complex enzymes in the oxidative phosphorylation pathway directly inhibits fungal pathogens. For example, ATP synthase is the universal enzyme responsible for ATP production, catalyzing the phosphorylation of ADP using inorganic phosphate (Pi); have been a target of drug development against pathogens. An antibiotic Bedaquiline exerts its antibacterial effect by specifically binding to the c-subunit of the FO rotor ring in the mycobacterial FOF1 ATP synthase. This interaction inhibits ATP production, leading to bacterial cell death [54]. Natamycin treatment impaired ATP production in M. oryzae by regulating key enzymes in the oxidative phosphorylation pathway, such as ATP synthase subunit alpha (MGG_07752). This inhibition of energy production consequently suppressed mycelial growth.

To validate OXPHOS as a critical target, we overexpressed MGG_17901 (cytochrome c oxidase subunit VIa) and MGG_04969 (electron transfer protein 1), both components of Complex IV, the terminal enzyme of the electron transport chain [55]. Overexpression of either gene conferred significant tolerance to natamycin (Fig. 7B-C), demonstrating that increasing expression of these pathway components can partially compensate for natamycin-induced suppression. Notably, the overexpression strains also exhibited enhanced pathogenicity (Fig. 7C-F), indicating that these OXPHOS genes contribute to M. oryzae virulence. This dual role inhibiting both growth and virulence, is a particularly attractive feature for a biocontrol agent. While transcriptomic and metabolomic changes alone cannot distinguish primary targets from secondary consequences, the rapid downregulation of OXPHOS genes and the significant tolerance conferred by overexpression of MGG_17901 and MGG_04969 provide functional evidence that oxidative phosphorylation is a critical, and likely a target pathway of natamycin in M. oryzae, but direct biochemical evidence (e.g., enzyme activity assays or binding studies) will be required to confirm whether natamycin directly interacts with these pathway components.

Conservation of natamycin-binding sites across diverse M. oryzae populations

An assessment of amino acid variation in the oxidative phosphorylation target proteins MGG_04969 and MGG_17901 across 96 M. oryzae strains from diverse hosts. This population-level analysis provides insights into the potential durability and broad-spectrum applicability of natamycin as a biocontrol agent [56, 57]. Our analysis revealed that natamycin-binding residues remained completely conserved in both proteins. For MGG_17901 (cytochrome c oxidase subunit VIa), the amino acid sequence was 100% conserved across all 96 strains (Fig. 8D), including the hydrogen-bonding residues ARG-90, ASN-97, ILE-98, VAL-118, and TYR-120. For MGG_04969 (electron transfer protein 1), while six mutant sites were identified (L3F, K64R, R297C, F354S, G379R, and V414I) (Fig. 8C), none occurred at the natamycin-binding residues ASN-231, SER-289, and SER-476. The conservation of these binding sites across diverse strains representing different host origins and genetic backgrounds suggests strong purifying selection acting on these functionally essential proteins [52, 55]. Studies have shown that M. oryzae populations are structured by host specialization and geographic origin [58, 59], yet key functional domains remain conserved. This functional constraint on cytochrome c oxidase and electron transfer proteins, fundamental to mitochondrial energy production, reduces the likelihood of resistance-conferring mutations arising spontaneously in field populations [60, 61]. The high conservation suggests broad-spectrum efficacy against diverse M. oryzae populations and a low risk of rapid target-site resistance development, although other resistance mechanisms (e.g., drug efflux or metabolic detoxification) could still emerge [62].

Despite the strong conservation observed at the population level, direct experimental validation of natamycin binding to MGG_17901 and MGG_04969 is essential to confirm these proteins as bona fide targets. We propose that future studies employ site-directed mutagenesis of the key residues predicted to mediate natamycin binding specifically ARG-90, ASN-97, ILE-98, VAL-118, and TYR-120 in MGG_17901, and ASN-231, SER-289, and SER-476 in MGG_04969. By systematically substituting these residues with alanine, mutant variants with diminished natamycin-binding capacity could be generated. These mutants could be introduced into a ΔMGG_17901 or ΔMGG_04969 knockout background via homologous recombination or CRISPR-Cas9, and the resulting strains evaluated for natamycin susceptibility.

In this study, we provide comprehensive evidence that the microbial compound natamycin has strong antifungal activity against M. oryzae, markedly inhibiting mycelial growth in vitro with an EC₅₀ value of 11.77 mg/L and reducing disease severity in planta to 4.1% of the control level. Notably, we highlight that the core mechanisms underlying this antifungal activity of natamycin are mainly by inducing a systemic metabolic crisis and potentially targeting essential biosynthetic and energy-generating pathways, including amino acid biosynthesis, oxidative phosphorylation and mitochondria pathways. Integrated transcriptomic and metabolomic analyses revealed that natamycin exerts its effect through a novel, multi-target mechanism, primarily by coordinately disrupting the oxidative phosphorylation pathway to cripple energy production and concurrently suppressing amino acid biosynthesis. Finally, the overexpression of two oxidative phosphorylation-related genes MGG_17901 and MGG_04969, conferred significant natamycin tolerance, genetically confirmed this pathway as a critical target. Overall findings emphasize the novel and potential mechanisms of using natamycin as a useful bioresource agent for the control of M. oryzae.

Authors' contributions

Conceptualization C.L. and Y.W; methodology, Y.X, C.W, R.C, Y.C, J.S, Q.L; data analysis, J.S.M, Y.W; R.L; writing the original manuscript, J.S.M; revision and editing, W.Y, C.Y; M.H.R.H; funding acquisition, C.L, Y.W All authors have read and agreed for the publication of the final manuscript.

Funding

This work was supported by the National Key R&D Program of China (2023YFD1400800).The National Natural Science Foundation of China (32560630, 32202254). Major Science and Technology Projects in Yunnan (202402AE090026).The Talents Training Program in Yunnan (YNYC-2022-0217). The Yunnan Provincial Talent Program for Wang Yi (XDYC.QNRC 2023 − 0421).

Data availability

The sequencing data are available at the NCBI SRA under the BioProject number PRJNA1061252 at https://www.ncbi.nlm.nih.gov/sra?LinkName=bioproject_sra_all&from_uid=1061252.

Declarations

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Competing interests

The authors declare no competing interests.